{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RNN.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "feUJkkKjMoV4",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IWTXM4EVJ0Vp",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "from collections import defaultdict\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "import sys\n",
        "import os\n",
        "os.environ['KERAS_BACKEND']='theano'\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import Dense, Input, Flatten\n",
        "from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout, LSTM, GRU, Bidirectional\n",
        "from keras.models import Model\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import matplotlib.pyplot as plt\n",
        "plt.switch_backend('agg')\n",
        "from keras import backend as K\n",
        "from keras.engine.topology import Layer, InputSpec\n",
        "from keras import initializers\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FMMZoBf5J1Th",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def clean_str(string):\n",
        "    string = re.sub(r\"\\\\\", \"\", string)\n",
        "    string = re.sub(r\"\\'\", \"\", string)\n",
        "    string = re.sub(r\"\\\"\", \"\", string)\n",
        "    return string.strip().lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xovVXTjFKLU8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "MAX_SEQUENCE_LENGTH = 1000\n",
        "MAX_NB_WORDS = 20000\n",
        "EMBEDDING_DIM = 100\n",
        "VALIDATION_SPLIT = 0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G0fj58BfKOu5",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a88cef11-1224-4d27-a2c2-e22a4f936eab",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790268406,
          "user_tz": -330,
          "elapsed": 1117,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# reading data\n",
        "df = pd.read_excel('dataset.xlsx')\n",
        "df = df.dropna()\n",
        "df = df.reset_index(drop=True)\n",
        "print('Shape of dataset ',df.shape)\n",
        "print(df.columns)\n",
        "print('No. of unique classes',len(set(df['class'])))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of dataset  (100, 2)\n",
            "Index(['message', 'class'], dtype='object')\n",
            "No. of unique classes 17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iwPHBhWXKToO",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "macronum=sorted(set(df['class']))\n",
        "macro_to_id = dict((note, number) for number, note in enumerate(macronum))\n",
        "\n",
        "def fun(i):\n",
        "    return macro_to_id[i]\n",
        "\n",
        "df['class']=df['class'].apply(fun)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y09w4GeuKYuT",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "67d55385-0539-4f9b-b7c2-06c3960c4acf",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790271019,
          "user_tz": -330,
          "elapsed": 1316,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "texts = []\n",
        "labels = []\n",
        "\n",
        "\n",
        "for idx in range(df.message.shape[0]):\n",
        "    text = BeautifulSoup(df.message[idx])\n",
        "    texts.append(clean_str(str(text.get_text().encode())))\n",
        "\n",
        "for idx in df['class']:\n",
        "    labels.append(idx)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html5lib\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
            "\n",
            "The code that caused this warning is on line 193 of the file /usr/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP})\n",
            "\n",
            "to this:\n",
            "\n",
            " BeautifulSoup(YOUR_MARKUP, \"html5lib\")\n",
            "\n",
            "  markup_type=markup_type))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "V4KRLKvPKd1R",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f96fc511-0562-434d-8382-6502a6783506",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790272094,
          "user_tz": -330,
          "elapsed": 978,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "print('Number of Unique Tokens',len(word_index))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Unique Tokens 932\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RnjM0mCxKyVi",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e4f25d93-f124-406c-daf6-cfbc7127988b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790273080,
          "user_tz": -330,
          "elapsed": 897,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "\n",
        "labels = to_categorical(np.asarray(labels))\n",
        "print('Shape of Data Tensor:', data.shape)\n",
        "print('Shape of Label Tensor:', labels.shape)\n",
        "\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]\n",
        "nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n",
        "\n",
        "x_train = data[:-nb_validation_samples]\n",
        "y_train = labels[:-nb_validation_samples]\n",
        "x_val = data[-nb_validation_samples:]\n",
        "y_val = labels[-nb_validation_samples:]\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Data Tensor: (100, 1000)\n",
            "Shape of Label Tensor: (100, 17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "c0aypSbJK_yN",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dfccfa98-1a77-4dc2-fa95-c5c9048fc2a9",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790287000,
          "user_tz": -330,
          "elapsed": 13830,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embeddings_index = {}\n",
        "f = open('glove.6B.100d.txt',encoding='utf8')\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "print('Total %s word vectors in Glove 6B 100d.' % len(embeddings_index))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total 400000 word vectors in Glove 6B 100d.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HQIY5wTZLDyT",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xKuWSVHcLVDM",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "embedding_layer = Embedding(len(word_index) + 1,\n",
        "                            EMBEDDING_DIM,\n",
        "                            weights=[embedding_matrix],\n",
        "                            input_length=MAX_SEQUENCE_LENGTH,\n",
        "                            trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eExWnv0ALYaG",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "b1c4b12e-3fbf-4c85-e67b-faab3ea88274",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530790300647,
          "user_tz": -330,
          "elapsed": 11390,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
        "embedded_sequences = embedding_layer(sequence_input)\n",
        "l_lstm = Bidirectional(LSTM(100))(embedded_sequences)\n",
        "preds = Dense(len(macronum), activation='softmax')(l_lstm)\n",
        "model = Model(sequence_input, preds)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['acc'])\n",
        "\n",
        "print(\"Bidirectional LSTM\")\n",
        "model.summary()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Bidirectional LSTM\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 1000)              0         \n",
            "_________________________________________________________________\n",
            "embedding_1 (Embedding)      (None, 1000, 100)         93300     \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 200)               160800    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 17)                3417      \n",
            "=================================================================\n",
            "Total params: 257,517\n",
            "Trainable params: 257,517\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S4xfBNtKLdD2",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 1074
        },
        "outputId": "5a3abcce-be20-4c71-c989-5ebc191f064d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792237299,
          "user_tz": -330,
          "elapsed": 1702650,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "cp=ModelCheckpoint('model_rnn.hdf5',monitor='val_acc',verbose=1,save_best_only=True)\n",
        "history=model.fit(x_train, y_train, validation_data=(x_val, y_val),epochs=15, batch_size=2,callbacks=[cp])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 80 samples, validate on 20 samples\n",
            "Epoch 1/15\n",
            "80/80 [==============================] - 168s 2s/step - loss: 2.4410 - acc: 0.2125 - val_loss: 1.9150 - val_acc: 0.4500\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.45000, saving model to model_rnn.hdf5\n",
            "Epoch 2/15\n",
            "80/80 [==============================] - 147s 2s/step - loss: 2.0052 - acc: 0.4750 - val_loss: 1.5308 - val_acc: 0.5500\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.45000 to 0.55000, saving model to model_rnn.hdf5\n",
            "Epoch 3/15\n",
            "80/80 [==============================] - 140s 2s/step - loss: 1.5564 - acc: 0.5750 - val_loss: 1.3702 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.55000 to 0.65000, saving model to model_rnn.hdf5\n",
            "Epoch 4/15\n",
            "80/80 [==============================] - 99s 1s/step - loss: 1.3451 - acc: 0.6125 - val_loss: 1.1095 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.65000\n",
            "Epoch 5/15\n",
            "48/80 [=================>............] - ETA: 28s - loss: 1.1962 - acc: 0.6667"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 76s 944ms/step - loss: 1.1779 - acc: 0.7000 - val_loss: 1.4087 - val_acc: 0.6000\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.65000\n",
            "Epoch 6/15\n",
            "80/80 [==============================] - 80s 1s/step - loss: 0.9999 - acc: 0.7250 - val_loss: 0.7506 - val_acc: 0.9000\n",
            "\n",
            "Epoch 00006: val_acc improved from 0.65000 to 0.90000, saving model to model_rnn.hdf5\n",
            "Epoch 7/15\n",
            "80/80 [==============================] - 100s 1s/step - loss: 0.7787 - acc: 0.7875 - val_loss: 0.7046 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00007: val_acc improved from 0.90000 to 0.95000, saving model to model_rnn.hdf5\n",
            "Epoch 8/15\n",
            "80/80 [==============================] - 96s 1s/step - loss: 0.7224 - acc: 0.8000 - val_loss: 0.5418 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00008: val_acc did not improve from 0.95000\n",
            "Epoch 9/15\n",
            "80/80 [==============================] - 136s 2s/step - loss: 0.5805 - acc: 0.8375 - val_loss: 0.5848 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.95000\n",
            "Epoch 10/15\n",
            " 4/80 [>.............................] - ETA: 2:09 - loss: 0.9108 - acc: 0.7500"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 127s 2s/step - loss: 0.6635 - acc: 0.8250 - val_loss: 0.4901 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00010: val_acc did not improve from 0.95000\n",
            "Epoch 11/15\n",
            "80/80 [==============================] - 103s 1s/step - loss: 0.5786 - acc: 0.8500 - val_loss: 0.4940 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00011: val_acc did not improve from 0.95000\n",
            "Epoch 12/15\n",
            "80/80 [==============================] - 102s 1s/step - loss: 0.4891 - acc: 0.9000 - val_loss: 0.6753 - val_acc: 0.9000\n",
            "\n",
            "Epoch 00012: val_acc did not improve from 0.95000\n",
            "Epoch 13/15\n",
            "80/80 [==============================] - 113s 1s/step - loss: 0.4893 - acc: 0.8875 - val_loss: 0.4899 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00013: val_acc did not improve from 0.95000\n",
            "Epoch 14/15\n",
            "80/80 [==============================] - 104s 1s/step - loss: 0.4824 - acc: 0.8750 - val_loss: 1.1208 - val_acc: 0.6500\n",
            "\n",
            "Epoch 00014: val_acc did not improve from 0.95000\n",
            "Epoch 15/15\n",
            "16/80 [=====>........................] - ETA: 1:26 - loss: 0.6188 - acc: 0.8750"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 111s 1s/step - loss: 0.4562 - acc: 0.8875 - val_loss: 0.4913 - val_acc: 0.9500\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.95000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qM-5003zLm4R",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "253775e8-4782-4df4-8113-061d06deabfa",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792305211,
          "user_tz": -330,
          "elapsed": 1410,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig1 = plt.figure()\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves :RNN',fontsize=16)\n",
        "fig1.savefig('loss_rnn.png')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2hj5paWkAlClTNkP7mjWrAM3iyCtCVOP8+XPExVn9ePHx8Zw6deKOOUNCSmdw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\nohBCCBHV/wOdNV/wiKUFawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff25faacc50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jlOpvtLtVNx5",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "0f68496c-d7d1-4a6f-d1b5-9fcefc3fb11f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530792327305,
          "user_tz": -330,
          "elapsed": 1471,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig2=plt.figure()\n",
        "plt.plot(history.history['acc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_acc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves : RNN',fontsize=16)\n",
        "fig2.savefig('accuracy_rnn.png')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mGt+kvHCfJXTsaPNHYLrXBPqnVwtwX2qR6ijLkFLmCSFeAA4BucAiKeW+CzWW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\nc7KBCD/fBJYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff25fa45630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VSLWHsVmkr-8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "#from keras.utils.vis_utils import plot_model\n",
        "#plot_model(model, to_file='rnn_model.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "22B4674Rkwko",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "60c9f2c1-1390-425a-8451-f68c4655fde0",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1530797392167,
          "user_tz": -330,
          "elapsed": 1204,
          "user": {
            "displayName": "Akshat Maheshwari",
            "photoUrl": "//lh5.googleusercontent.com/-f-xJkriVoaI/AAAAAAAAAAI/AAAAAAAAAVQ/TLGa4qObGgQ/s50-c-k-no/photo.jpg",
            "userId": "114426356464940466000"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "display(Image.open('rnn_model.png'))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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88cYbKC0tVcpu374NZ2dnvPTSS0Z127Vrh88++6yOIyQiejgmV0RkU1q3bo1L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            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=599x405 at 0x7FF25F552358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zdU_K2hMomOJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}